Artificial intelligence and machine learning for medical imaging: A technology review
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …
of disruptive technical advances and impressive experimental results, notably in the field of …
A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …
clinical application. Specifically, we summarized the recent developments of deep learning …
[HTML][HTML] Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance
L Vandewinckele, M Claessens, A Dinkla… - Radiotherapy and …, 2020 - Elsevier
Artificial Intelligence (AI) is currently being introduced into different domains, including
medicine. Specifically in radiation oncology, machine learning models allow automation and …
medicine. Specifically in radiation oncology, machine learning models allow automation and …
Adaptive radiation therapy (ART) strategies and technical considerations: a state of the ART review from NRG oncology
CK Glide-Hurst, P Lee, AD Yock, JR Olsen… - International Journal of …, 2021 - Elsevier
The integration of adaptive radiation therapy (ART), or modifying the treatment plan during
the treatment course, is becoming more widely available in clinical practice. ART offers …
the treatment course, is becoming more widely available in clinical practice. ART offers …
Deep learning based synthetic‐CT generation in radiotherapy and PET: a review
Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …
computed tomography (sCT) have received significant research attention as an alternative to …
Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography
Purpose The incorporation of cone‐beam computed tomography (CBCT) has allowed for
enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images …
enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images …
CBCT‐Based synthetic CT image generation using conditional denoising diffusion probabilistic model
Background Daily or weekly cone‐beam computed tomography (CBCT) scans are
commonly used for accurate patient positioning during the image‐guided radiotherapy …
commonly used for accurate patient positioning during the image‐guided radiotherapy …
CBCT‐based synthetic CT generation using deep‐attention cycleGAN for pancreatic adaptive radiotherapy
Purpose Current clinical application of cone‐beam CT (CBCT) is limited to patient setup.
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …
Adaptive proton therapy
Radiation therapy treatments are typically planned based on a single image set, assuming
that the patient's anatomy and its position relative to the delivery system remains constant …
that the patient's anatomy and its position relative to the delivery system remains constant …
Creating artificial images for radiology applications using generative adversarial networks (GANs)–a systematic review
Rationale and Objectives Generative adversarial networks (GANs) are deep learning
models aimed at generating fake realistic looking images. These novel models made a great …
models aimed at generating fake realistic looking images. These novel models made a great …